In this tutorial, I will walk through some of the basic steps needed to process the Sentinel-1 SAR data using SNAP. For this tutorial, I am using SNAP version 7.0. You can check the version of the SNAP from Help -> About SNAP…
In this tutorial, we will be adding the attribute to the shapefile and updating its value using ArcPy. You need to have ArcGIS installed in order to use ArcPy. This script has been tested with ArcGIS Version 10.6 but should work with most versions.
Go ahead and download the data from here. This data … CONTINUE READING
In this tutorial, we are going to write a simple script to zip your shapefiles so you can upload them to Google Earth Engine (GEE). If you are following along, refer to this post, you can see that GEE takes in certain kind of files in for it to be valid shapefile. Alternatively, you … CONTINUE READING
The Map Projection System used in Nepal is the MUTM (Modified Universal Transverse Mercator). Read more about Map Projection System in Nepal here.
In this exercise, I will be using ArcGIS. Other methods will be posted in the future.
Estimates suggest that only 30 percent of the world’s population has access to formal land administration systems to register and protect their land rights. Surveying and mapping cadastral boundaries using traditional, field-based methods is accurate but can prove to be extremely time, cost and labor-intensive. This makes it difficult to create or update existing cadastral … CONTINUE READING
Use the following code or follow the link… CONTINUE READING
The full code for classification after obtaining sample points is here. The full code for classification with sample points generated on the fly is here.
Note: This is a very simple method for generating Landcover maps meant for showing the basic workflow for generating Landcover maps. You can use more sophisticated algorithms like … CONTINUE READING
In this exercise, we will try to sample a composite image of the Landsat with the landcover feature collection that we have. The full code can be found here.
The Feature Collection has the following integer assigned for ‘landcover’ column.
0 -> urban
1 -> vegetation
2 -> water
The full source code for the same can be found here.
Maldives, Myanmar, and the Philippines are vulnerable to natural disasters. Sendai Framework of Action calls for risk reduction by implementing early warning systems. A prevailing challenge is for authorities to coordinate warnings across disparate communication systems and autonomous organizations. Cross-Agency Situational-Awareness platforms and the ITU-T X.1303 Common Alerting Protocol (CAP) interoperable data standards present themselves … CONTINUE READING
In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale and is based on the BACI (Before-After, Control-Impact) design. This approach utilizes Google Earth Engine, a state-of-the-art cloud computing platform. A web-based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index(EVI) from Moderate Resolution … CONTINUE READING
Transferring datasets between different buckets in Google Cloud Storage
Operational Flood Risk Index Mapping for Disaster Risk Reduction using Earth Observations and Cloud Computing Technologies: a Case Study on Myanmar
Linking Earth Observations for Assessing the Food Security Situation in Vietnam: a Landscape Approach
Convert the GPS waypoints to Shapefile
Ditch Snappy to use Graph Processor Tool (GPT) to process your Sentinel-1 Data
Land cover Mapping in Data Scarce Environments: Challenges and Opportunities. Another paper on Land Cover
Primitives as building blocks for constructing landcover maps – New paper out on RLCMS and Google Earth Engine
Downloading 12.5 m ALOS PALSAR High-Resolution DEM
Changing the size of the rectangle in Google Earth Engine
Connected Pixel Counts in Google Earth Engine
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